{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a825ba6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================================BUG REPORT===================================\n",
      "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
      "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
      "================================================================================\n",
      "CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n",
      "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
      "CUDA SETUP: Detected CUDA version 117\n",
      "CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "import os\n",
    "\n",
    "import torch\n",
    "from torch.optim import AdamW\n",
    "from torch.utils.data import DataLoader\n",
    "from peft import (\n",
    "    get_peft_config,\n",
    "    get_peft_model,\n",
    "    get_peft_model_state_dict,\n",
    "    set_peft_model_state_dict,\n",
    "    PeftType,\n",
    "    PrefixTuningConfig,\n",
    "    PromptEncoderConfig,\n",
    ")\n",
    "\n",
    "import evaluate\n",
    "from datasets import load_dataset\n",
    "from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2bd7cbb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "model_name_or_path = \"roberta-large\"\n",
    "task = \"mrpc\"\n",
    "peft_type = PeftType.P_TUNING\n",
    "device = \"cuda\"\n",
    "num_epochs = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33d9b62e",
   "metadata": {},
   "outputs": [],
   "source": [
    "peft_config = PromptEncoderConfig(task_type=\"SEQ_CLS\", num_virtual_tokens=20, encoder_hidden_size=128)\n",
    "lr = 1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "152b6177",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset glue (/home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a451b90675e0451489cc6426465afa32",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-9fa7887f9eaa03ae.arrow\n",
      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-dc593149bbeafe80.arrow\n",
      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-140ebe5b70e09817.arrow\n"
     ]
    }
   ],
   "source": [
    "if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
    "    padding_side = \"left\"\n",
    "else:\n",
    "    padding_side = \"right\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
    "if getattr(tokenizer, \"pad_token_id\") is None:\n",
    "    tokenizer.pad_token_id = tokenizer.eos_token_id\n",
    "\n",
    "datasets = load_dataset(\"glue\", task)\n",
    "metric = evaluate.load(\"glue\", task)\n",
    "\n",
    "\n",
    "def tokenize_function(examples):\n",
    "    # max_length=None => use the model max length (it's actually the default)\n",
    "    outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
    "    return outputs\n",
    "\n",
    "\n",
    "tokenized_datasets = datasets.map(\n",
    "    tokenize_function,\n",
    "    batched=True,\n",
    "    remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
    ")\n",
    "\n",
    "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
    "# transformers library\n",
    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
    "\n",
    "\n",
    "def collate_fn(examples):\n",
    "    return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
    "\n",
    "\n",
    "# Instantiate dataloaders.\n",
    "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
    "eval_dataloader = DataLoader(\n",
    "    tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6bc8144",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
    "model = get_peft_model(model, peft_config)\n",
    "model.print_trainable_parameters()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af41c571",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = AdamW(params=model.parameters(), lr=lr)\n",
    "\n",
    "# Instantiate scheduler\n",
    "lr_scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer=optimizer,\n",
    "    num_warmup_steps=0,  # 0.06*(len(train_dataloader) * num_epochs),\n",
    "    num_training_steps=(len(train_dataloader) * num_epochs),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90993c93",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                                  | 0/115 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0: {'accuracy': 0.6985294117647058, 'f1': 0.8172362555720655}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     "text": [
      "epoch 1: {'accuracy': 0.6936274509803921, 'f1': 0.806201550387597}\n"
     ]
    },
    {
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     ]
    },
    {
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     "text": [
      "epoch 2: {'accuracy': 0.7132352941176471, 'f1': 0.8224582701062216}\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "text": [
      "epoch 3: {'accuracy': 0.7083333333333334, 'f1': 0.8199697428139183}\n"
     ]
    },
    {
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      "epoch 4: {'accuracy': 0.7205882352941176, 'f1': 0.8246153846153846}\n"
     ]
    },
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      "epoch 5: {'accuracy': 0.7009803921568627, 'f1': 0.8200589970501474}\n"
     ]
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      "epoch 6: {'accuracy': 0.7254901960784313, 'f1': 0.8292682926829268}\n"
     ]
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      "epoch 7: {'accuracy': 0.7230392156862745, 'f1': 0.8269525267993874}\n"
     ]
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      "epoch 8: {'accuracy': 0.7254901960784313, 'f1': 0.8297872340425533}\n"
     ]
    },
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      "epoch 9: {'accuracy': 0.7230392156862745, 'f1': 0.828006088280061}\n"
     ]
    },
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      "epoch 10: {'accuracy': 0.7181372549019608, 'f1': 0.8183254344391785}\n"
     ]
    },
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      "epoch 11: {'accuracy': 0.7132352941176471, 'f1': 0.803361344537815}\n"
     ]
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      "epoch 12: {'accuracy': 0.7107843137254902, 'f1': 0.8206686930091186}\n"
     ]
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      "epoch 13: {'accuracy': 0.7181372549019608, 'f1': 0.8254931714719272}\n"
     ]
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      "epoch 14: {'accuracy': 0.7156862745098039, 'f1': 0.8253012048192772}\n"
     ]
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      "epoch 17: {'accuracy': 0.7107843137254902, 'f1': 0.8217522658610272}\n"
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    },
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     "text": [
      "epoch 18: {'accuracy': 0.7254901960784313, 'f1': 0.8292682926829268}\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "epoch 19: {'accuracy': 0.7107843137254902, 'f1': 0.8206686930091186}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model.to(device)\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    for step, batch in enumerate(tqdm(train_dataloader)):\n",
    "        batch.to(device)\n",
    "        outputs = model(**batch)\n",
    "        loss = outputs.loss\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "    model.eval()\n",
    "    for step, batch in enumerate(tqdm(eval_dataloader)):\n",
    "        batch.to(device)\n",
    "        with torch.no_grad():\n",
    "            outputs = model(**batch)\n",
    "        predictions = outputs.logits.argmax(dim=-1)\n",
    "        predictions, references = predictions, batch[\"labels\"]\n",
    "        metric.add_batch(\n",
    "            predictions=predictions,\n",
    "            references=references,\n",
    "        )\n",
    "\n",
    "    eval_metric = metric.compute()\n",
    "    print(f\"epoch {epoch}:\", eval_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a43bd9fb",
   "metadata": {},
   "source": [
    "## Share adapters on the 🤗 Hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "871b75aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/smangrul/roberta-large-peft-p-tuning/commit/fa7abe613f498c76df5e16c85d9c19c3019587a7', commit_message='Upload model', commit_description='', oid='fa7abe613f498c76df5e16c85d9c19c3019587a7', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.push_to_hub(\"smangrul/roberta-large-peft-p-tuning\", use_auth_token=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6a9036",
   "metadata": {},
   "source": [
    "## Load adapters from the Hub\n",
    "\n",
    "You can also directly load adapters from the Hub using the commands below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91b0b8f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.decoder.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.bias', 'roberta.pooler.dense.weight', 'lm_head.layer_norm.weight', 'roberta.pooler.dense.bias', 'lm_head.dense.weight', 'lm_head.bias']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e650799d58ec4bd1b21b6bc28ddf2069",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/4.29M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                                   | 0/13 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  7.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': 0.7107843137254902, 'f1': 0.8206686930091186}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from peft import PeftModel, PeftConfig\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "peft_model_id = \"smangrul/roberta-large-peft-p-tuning\"\n",
    "config = PeftConfig.from_pretrained(peft_model_id)\n",
    "inference_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)\n",
    "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
    "\n",
    "# Load the Lora model\n",
    "inference_model = PeftModel.from_pretrained(inference_model, peft_model_id)\n",
    "\n",
    "inference_model.to(device)\n",
    "inference_model.eval()\n",
    "for step, batch in enumerate(tqdm(eval_dataloader)):\n",
    "    batch.to(device)\n",
    "    with torch.no_grad():\n",
    "        outputs = inference_model(**batch)\n",
    "    predictions = outputs.logits.argmax(dim=-1)\n",
    "    predictions, references = predictions, batch[\"labels\"]\n",
    "    metric.add_batch(\n",
    "        predictions=predictions,\n",
    "        references=references,\n",
    "    )\n",
    "\n",
    "eval_metric = metric.compute()\n",
    "print(eval_metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a8d69d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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